D. V. Vinogradov

553 citations
30 papers · 328 indexed · h-index 8
Topics
Rough Sets and Fuzzy Logic (11 papers)Machine Learning and Algorithms (5 papers)Statistical and Computational Modeling (4 papers)
Journals
SHILAP Revista de lepidopterologíaInternational Journal of Molecular SciencesMolecules

In The Last Decade

D. V. Vinogradov

22 papers receiving 318 citations

Peers

D. V. Vinogradov
Comparison fields: 5 of 84
  • Molecular Biology 190
  • Plant Science 91
  • Genetics 66
  • Food Science 34
  • Computational Theory and Mathematics 28
Replace Chern Han Yong with:
Chern Han Yong Singapore
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Rakesh Kaundal United States
Aleksandra Gruca Poland
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Citations per field
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Citations per year

Countries citing papers authored by D. V. Vinogradov

Since Specialization
Citations

This map shows the geographic impact of D. V. Vinogradov's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by D. V. Vinogradov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. V. Vinogradov more than expected).

Fields of papers citing papers by D. V. Vinogradov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D. V. Vinogradov. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by D. V. Vinogradov. The network helps show where D. V. Vinogradov may publish in the future.

Co-authorship network of co-authors of D. V. Vinogradov

This figure shows the co-authorship network connecting the top 25 collaborators of D. V. Vinogradov. A scholar is included among the top collaborators of D. V. Vinogradov based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with D. V. Vinogradov. D. V. Vinogradov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 1
3 1
4 1
5 11
6 3
7
Continuous Attributes for FCA-based Machine Learning.
0
8 0
9 1
10 2
11 0
12 6
13 0
14 10
15 3
16 18
17 128
18 7
19
RegTransBase - A Database Of Regulatory Sequences and Interactions in a Wide Range of \nProkaryotic Genomes
81
20 6

About D. V. Vinogradov

D. V. Vinogradov is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 30 papers that have together received 328 indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (11 papers), Machine Learning and Algorithms (5 papers) and Statistical and Computational Modeling (4 papers). The work is most often cited by research in Horticulture (6 citations), Molecular Biology (190 citations) and Plant Science (91 citations). D. V. Vinogradov has collaborated with scholars based in Russia, Tajikistan and United States. Frequent co-authors include Mikhail S. Gelfand, Maria D. Logacheva, Vsevolod J. Makeev, Tahir H. Samigullin, Aleksey A. Penin, Artem S. Kasianov, Pavel S. Novichkov, Simon Minovitsky, Alexey E. Kazakov and Adam P. Arkin. Their work appears in journals such as SHILAP Revista de lepidopterología, International Journal of Molecular Sciences and Molecules.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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